Biology subjects

Roascio, M.

Publications and source records attributed to Roascio, M..

8 recordsLinked to original sources

bioRxiv2026

REM sleep reconfigures large-scale network dynamics: a link to its suppressive role in epilepsy

Converging evidence suggests that human brain activity operates near a critical-like regime in which balanced excitation and inhibition support efficient large-scale communication. The brains proximity to criticality may be dynamically reset across the sleep-wake cycle and altered by epilepsy, leading to aberrant oscillatory dynamics. Building on recent work demonstrating a tripartite interaction between networks synchronization, oscillatory amplitude bistability, and cross-frequency coupling in the human brain that seems to favour epileptic activity, we examined how this interaction, and its underlying large-scale dynamics are modulated across vigilance states. We analyzed overnight recordings from 20 patients with drug-resistant epilepsy undergoing presurgical evaluation and selected overall 20 minutes of continuous, artifact free stereo-electroencephalography (SEEG) spanning REM sleep, NREM stages N2 and N3, and eyes-closed resting wakefulness. Across states, we quantified phase synchronization, phase-amplitude coupling, bistability and their correlation. REM sleep was consistently associated with a reduction of these dynamics relative to NREM sleep and wakefulness. Importantly, the canonical correlation between these measures -- reflecting the strength of the tripartite interaction -- was significantly weaker during REM sleep. These findings indicate that vigilance states modulate this previously identified multiscale interaction in human brain networks and suggest that the reduced epileptogenicity of REM sleep can be associated with a disruption of coordinated synchronization, coupling, and bistable dynamics at the large-scale network level.

neuroscience↗

bioRxiv2026

CROCOpy - A Python toolbox for the analysis of CRitical Oscillations and COnnectivity

CROCOpy is a light-weighted toolbox for the assessment of neuronal oscillations, and multiple observables of functional connectivity (phase synchronization, amplitude coupling, and cross-frequency coupling) and critical dynamics (avalanches, long-range temporal correlations, bistability, and functional excitation-inhibition ratio). It was developed to simplify the analysis of continuous electrophysiological recordings and, in addition to metric computation, also includes methods for narrow-band filtering and statistical analysis. It is device-agnostic and supports both GPU and CPU computations. The toolbox also provides detailed tutorials.

neuroscience↗

bioRxiv2025

Quantifying cortical maturational aspects during different vigilance states in preterm infants by advanced EEG analysis

Preterm birth is associated with numerous neurodevelopmental adverse outcomes, even in the absence of acquired lesions, as it occurs during a critical period of brain development. Clear organization of vigilance states can be recognized from 30-32 weeks postmenstrual age (PMA). In this study, we investigated whether spatial and temporal properties of neuronal oscillatory dynamics (i.e., phase synchronization, bistability, and cross-frequency coupling) during different vigilance states provide insights into cortical maturation in preterm infants born very low birth weight (VLBW) at low neurological risk and devoid of detectable brain lesions. We analyzed artifact-free video-polysomnographic data from 11 VLBW preterm infants (PMA at recording: 33.0 {+/-} 1.6 weeks) who underwent brain MRI at term-equivalent age. For each vigilance state, we computed the weighted Phase Lag Index (wPLI), Bistability Index (BiS), and Phase-Amplitude Coupling (PAC), both globally and across anterior and posterior regions, and examined their correlation with PMA at recording. wPLI, BiS, and PAC showed specific trends across vigilance states. BiS and PAC exhibited posterior-to-anterior differences and correlated with PMA. Our study suggests that these electrophysiological markers, particularly BiS and PAC, may serve as indices to monitor aspects of cortical maturation in VLBW at low neurological risk.

neuroscience↗

bioRxiv2025

Sleep-Modulated Cross-Frequency Coupling Between δ Phase and β-γ Bistability: A System-Level Modulation of Epileptic Activity

ObjectiveWhile slow waves in {delta} (0.5-4 Hz) characterize NREM sleep, in patients with sleep-related epilepsy, seizures most frequently emerge during NREM stage 2, known to be promoted by {delta}-band instability. Meanwhile, the epileptogenic zone (EZ) shows localized bistability in {beta}-{gamma} band (15-200 Hz) neuronal oscillations--indicating a catastrophic shift toward seizure. We aim to clarify the mechanistic link between {delta}-band synchrony and {beta}-{gamma} band bistability in epilepsy. MethodsWe studied a cohort of fourteen patients with Sleep Hypermotor Epilepsy (22.3 {+/-} 10.8 years old; 7 males). 7-9-hour stereo-EEG sleep recordings were segmented into 10-minute of uninterrupted, interictal N2 and N3 epochs, and phase synchrony, phase-amplitude coupling (PAC), and bistability were assessed. Canonical correlation was examined to answer whether PAC links {delta}-phase to {beta}-{gamma} bistability. ResultsCompared to non-EZ, the EZ exhibited larger 15-200 Hz bistability along with stronger 2-8 Hz and 15-100 Hz synchrony throughout N2 and N3. Compared to N3, N2 showed stronger PAC between 2-30 Hz phases in the non-EZ and 5-150 Hz amplitudes in the EZ. Canonical correlations between {delta}-phase modulated PAC and both bistability and synchrony were identified during N2 (r = 0.86 and 0.82) and N3 (r = 0.84 and 0.80), with the strongest contributors being 2-4 Hz synchrony and bistability in 2-4 Hz and 15-200 Hz bands. Correlations between interictal spikes and canonical covariates of bistability and PAC (r2 = 0.62 for N2 and 0.56 for N3) validated their relevance to epileptogenicity. Significance{delta}-band synchrony and {beta}-{gamma} band bistability are not isolated epileptogenic mechanisms but likely act synergistically, playing a pivotal role in seizure generation through the coupling of {delta} phases and {beta}-{gamma} amplitudes across large networks, with significant contributions from non-epileptogenic tissues. Key pointsO_LIStrong {beta}-{gamma} bistability in neuronal oscillations localizes the EZ throughout N2 and N3 sleep. C_LIO_LIElevated {delta}-band phase synchrony characterizes the EZ and its functional neighbors throughout N2 and N3 sleep. C_LIO_LIa-band synchrony modulates local {beta}-{gamma} bistability through PAC, with significant contributions from non-EZ tissues. C_LI

neuroscience↗

bioRxiv2023

Altered Brain Dynamics in idiopathic REM sleep behavior disorder: Implications for a continuum from prodromal to overt alpha-synucleinopathies

Idiopathic/isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of alpha-synucleinopathies. Cortical and sub-cortical brain modifications begin years before the emergence of overt neurodegenerative symptoms. To better understand the pathophysiological process impacting the brain from the prodromal to the overt stage of alpha-synucleinopathy, it is essential to assess iRBD patients over time. Recent evidence suggests that the human brain operates at an operating point near a critical phase transition between subcritical and supercritical phases in the systems state space to maintain cognitive and physiological performance. In contrast, a deviation from the critical regime leading to altered oscillatory dynamics has been observed in several pathologies. Here, we investigated if the alpha-synucleinopathy produces a deviation of the operating point already evident in the prodromal phase and if this shift correlates with biological and clinical disease severity. We analyzed a dataset of 59 patients with iRBD (age 69.61 {+/-} 6.98, 50 male) undergoing resting-state high-density EEG, presynaptic dopaminergic imaging, and clinical evaluations. Thirty-one patients (age 72.41 {+/-} 7.05, 31 male) also underwent clinical and instrumental follow-up (mean follow-up period 25.85 {+/-} 10.20 months). To localize the individual operating points along the excitation-inhibition (EI) continuum, we assessed both measures of neuronal EI balance and measures of critical brain dynamics such as long-range temporal correlation (LRTCs) and neuronal bistability in spontaneous narrow-band oscillations. Finally, we correlated critical brain dynamics and EI balance metrics with phase synchronization, nigro-striatal dopaminergic functioning, and clinical performances. Compared to 48 healthy subjects (age 70.25 {+/-} 10.15, 23 male), iRBD patients showed higher values of LRTCs and bistability in the 2-7 Hz band at diagnosis. Patients who eventually phenoconverted to overt alpha-synucleinopathy exhibited a more excitation-dominated (fEI > 1) condition than stable iRBD patients in 5-7 Hz. This higher excitation also directly correlated with phase synchronization in 2-7 Hz, further suggesting a shift of the operating point toward a supercritical state with the disease progression. Moreover, excitation-dominated state and low bistability were associated with deterioration of the nigro-striatal dopaminergic function and tended to correlate with stronger clinical symptoms. In conclusion, this study shows for the first time a deviation of the working point from inhibition-to excitation-dominated states along the continuum from prodromal to overt phases of the disease. These cortical brain dynamics modifications are associated with nigro-striatal dopaminergic impairment. These results increase our knowledge of the physiopathological process underlying alpha-synucleinopathies since prodromal stages, possibly providing new clues on disease-modifying strategies.

bioengineering↗

bioRxiv2023

Data-driven characterization of Preterm Birth through intramodal Diffusion MRI

Preterm birth still represents a concrete emergency to be managed and addressed globally. Since cerebral white matter injury is the major form of brain impairment in survivors of premature birth, the identification of reliable, non-invasive markers of altered white matter development is of utmost importance in diagnostics. Diffusion MRI has recently emerged as a valuable tool to investigate these kinds of alterations. In this work, rather than focusing on a single MRI modality, we worked on a compound of beyond-DTI High Angular Resolution Diffusion Imaging (HARDI) techniques in a group of 46 preterm babies studied on a 3T scanner at term equivalent age and in 23 control neonates born at term. After extracting relevant derived parameters, we examined discriminative patterns of preterm birth through (i) a traditional voxel-wise statistical method such as the Tract-Based Spatial Statistics approach (TBSS); (ii) an advanced Machine Learning approach such as the Support Vector Machine (SVM) classification; (iii) establishing the degree of association between the two methods in voting white matter most discriminating areas. Finally, we applied a multi-set Canonical Correlation Analysis (CCA) in search for sources of linked alterations across modalities. TBSS analysis showed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. SVM classification performed on skeletonized HARDI measures produced satisfactory accuracy rates, especially as for highly informative parameters about fibers directionality. Assessment of the degree of overlap between the relevant measures identified by the two methods exhibited a good, though parameter-dependent rate of agreement. Finally, CCA analysis identified joint changes precisely for those features exhibiting less correspondence between TBSS and SVM. Our results suggest that a data-driven intramodal imaging approach is crucial to extract deep and complementary information that cannot be extracted from a single modality.

bioengineering↗

bioRxiv2022

Large-scale network metrics improve the classification performance of rapid-eye-movement sleep behavior disorder patients

Clinical decision support systems based on machine-learning algorithms are largely applied in the context of the diagnosis of neurodegenerative diseases (NDDs). While recent models yield robust classifications in supervised two classes-problems accurately separating Parkinsons disease (PD) from healthy control (HC) subjects, few works looked at prodromal stages of NDDs. Idiopathic Rapid-eye Movement (REM) sleep behavior disorder (iRBD) is considered a prodromal stage of PD with a high chance of phenoconversion but with heterogeneous symptoms that hinder accurate disease prediction. Machine learning (ML) based methods can be used to develop personalized trajectory models, but these require large amounts of observational points with homogenous features significantly reducing the possible imaging modalities to non-invasive and cost-effective techniques such as high-density electrophysiology (hdEEG). In this work, we aimed at quantifying the increase in accuracy and robustness of the classification model with the inclusion of network-based metrics compared to the classical Fourier-based power spectral density (PSD). We performed a series of analyses to quantify significance in cohort-wise metrics, the performance of classification tasks, and the effect of feature selection on model accuracy. We report that amplitude correlation spectral profiles show the largest difference between iRBD and HC subjects mainly in delta and theta bands. Moreover, the inclusion of amplitude correlation and phase synchronization improves the classification performance by up to 11% compared to using PSD alone. Our results show that hdEEG features alone can be used as potential biomarkers in classification problems using iRBD data and that large-scale network metrics improve the performance of the model. This evidence suggests that large-scale brain network metrics should be considered important tools for investigating prodromal stages of NDD as they yield more information without harming the patient, allowing for constant and frequent longitudinal evaluation of patients at high risk of phenoconversion. HighlightsO_LINetwork-based features are important tools to investigate prodromal stages of PD C_LIO_LIAmplitude correlation shows the largest difference between two groups in 9/30 bands C_LIO_LIAmplitude correlation improved up to 11% the performance compared to PSD alone C_LIO_LIClassification robustness increases when we use both network-based EEG features C_LIO_LIClassifier performance worsens when PSD is added to network-based EEG features C_LI

bioengineering↗

bioRxiv2021

Phase and amplitude correlations change with disease progression in idiopathic Rapid eye-movement sleep behavior disorder patients

Cognitive decline is a common trait of neurodegenerative diseases of central nervous system and one of the major risk factors associated with faster phenoconversion from prodromal stages. In the transition to full-blown clinical syndromes, increased phase synchronization in the theta, alpha or beta EEG rhythms is thought to reflect the activation of compensatory mechanisms that may counterbalance the cognitive decline of patients affected by Mild Cognitive Impairment (MCI). Patients suffering from idiopathic Rapid eye-movement sleep Behavior Disorder (iRBD) have high risk of developing Parkinson Disease (PD) or Dementia with Lewy Bodies (DLB) and cognitive impairment is among the strongest risk factors together with motor symptoms. Here we wanted to investigate whether altered phase synchronization and amplitude couplings of the brain oscillations could be linked to the balancing of cognitive decline in a longitudinal cohort (N=18) of iRBD patients. We measured high-density Electroencephalographic (HD-EEG) activity at baseline and follow-up and quantified power distribution, orthogonalized amplitude correlation and weighted phase lag index. Despite the overt neurodegenerative progression (three patients converted to PD and one to DLB), cognitive decline was not evident from Mini Mental State Examination (MMSE) or neuropsychological tests. On the other hand, alpha phase synchronization and delta amplitude correlations were significantly different at follow-up compared to baseline. In particular, alpha synchrony was enhanced while delta amplitude coupling was reduced. Those differences were more pronounced among central-posterior channels while frontal channels showed a reduced number of significant edges with respect to surrogates. Both large-scale amplitude and phase coupling significantly correlated with cognitive or neuropsychological scores but not with sleep quality indices. Altogether, these results suggest that increased alpha phase-synchronization and reduced delta amplitude correlation may be considered as electrophysiological signs of an active compensatory mechanism of the cognitive impairment in RDB patients. Large-scale functional modifications could thus be used as significant biomarker in the characterization of prodromal stages of PD. Statement of SignificanceCognitive impairment and RBD emerge much earlier than the better-known motor symptoms distinctive of synucleinopathies. An improved investigation of RBD may constitute an important biomarker for an early diagnosis of the actual neurodegenerative diseases. For the first time, this preliminary study aims to quantify the large-scale network couplings as electrophysiological manifestation of the compensatory mechanism to the cognitive impairment in a longitudinal study of idiopathic RBD patients. Unfortunately, the small number of the subjects limits the generalizability of our observations, but this is only preliminary works in a larger project that aims to investigate advanced electrophysiological markers for an early diagnosis of the synucleinopathies.

bioengineering↗